Expanding a Database of Portuguese Tweets

This paper describes an existing database of geolocated tweets that were produced in Portuguese regions and proposes an approach to further expand it. The existing database covers eight consecutive days of collected tweets, totaling about 300 thousand tweets, produced by about 11 thousand different users. A detailed analysis on the content of the messages suggests a predominance of young authors that use Twitter as a way of reaching their colleagues with their feelings, ideas and comments. In order to further characterize this community of young people, we propose a method for retrieving additional tweets produced by the same set of authors already in the database. Our goal is to further extend the knowledge about each user of this community, making it possible to automatically characterize each user by the content he/she produces, cluster users and open other possibilities in the scope of social analysis

Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions

The information published by the millions of public social network users is an important source of knowledge that can be used in academic, socioeconomic or demographic studies (distribution of male and female population, age, marital status, birth), lifestyle analysis (interests, hobbies, social habits) or be used to study online behavior (time spent online, interaction with friends or discussion about brands, products or politics). This work uses a database of about 27 million Portuguese geolocated tweets, produced in Portugal by 97.8 K users during a 1-year period, to extract information about the behavior of the geolocated Portuguese Twitter community and show that with this information it is possible to extract overall indicators such as: the daily periods of increased activity per region; prediction of regions where the concentration of the population is higher or lower in certain periods of the year; how do regional habitants feel about life; or what is talked about in each region. We also analyze the behavior of the geolocated Portuguese Twitter users based on the tweeted contents, and find indications that their behavior differs in certain relevant aspect from other Twitter communities, hypothesizing that this is in part due to the abnormal high percentage of young teenagers in the community. Finally, we present a small case study on Portuguese tourism in the Algarve region. To the best of our knowledge, this work is the first study that shows geolocated Portuguese users' behavior in Twitter focusing on geographic regional use.info:eu-repo/semantics/acceptedVersio

MISNIS: an intelligent platform for Twitter topic mining

Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones.info:eu-repo/semantics/publishedVersio

Classificação prosódica de marcadores discursivos

This work describes the discourse markers present in two corpora for European Portuguese, in different domains (university lectures and map-task dialogues). In this study, we also perform a multiclass automatic classification task based on prosodic features to verify in both corpora which words are discourse markers, which are disfluencies, and which are sentence like-units (SUs). Results show that the selection of discourse markers varies across domain and between speakers. As for the classification task, results show that the discourse markers are better classified in the lectures corpus (87%) than in the dialogue corpus (84%). However, cross-domain experiments evidenced that data trained with the dialogue corpus predicts better the events in the lecture corpus, since this domain displays more speakers and therefore complex patterns. In both corpora, markers are more easily classified as SUs than as disfluencies.info:eu-repo/semantics/publishedVersio

Twitter: de espaço de mobilização a espaço de comentário. O caso do meet do Vasco da Gama

Este artigo interdisciplinar tem por objetivo analisar o papel do Twitter no meet do centro comercial Vasco da Gama de 20 de agosto de 2014, o qual se tornou particularmente mediático, trazendo para a opinião pública o debate sobre este tipo de eventos. Para tal, começamos por refletir sobre a multiplicidade dos usos do Twitter, pela sua utilização pela população mais jovem e também sobre o fenómeno dos meets e a sua finalidade. Em seguida analisamos o conteúdo de 1976 tweets publicados entre os dias 14 e 24 de agosto, que foram filtrados de forma automática recorrendo a um método de classificação designado por Fuzzy Fingerprints (Impressões Digitais Difusas) com base em hashtags e palavras-chave relevantes sobre o tema. Esta análise empírica mostra-nos, por um lado, o Twitter como plataforma de mobilização e comentário, potencializadora de interações online/offline e que é utilizada pelos jovens como fonte de distinção social. Por outro lado, os tweets partilhados em torno do meet ilustram de modo expressivo os processos culturais, linguísticos e sociais que estão por detrás deste fenómeno.info:eu-repo/semantics/publishedVersio